Redstone Foods Mm Wholesale Case Study Sales Forecasting

Case Redstone Foods Mm Wholesale Case Studysales Forecastingthis Exe

Case: Redstone Foods M&M Wholesale Case Study Sales Forecasting This exercise provides you the opportunity to apply several concepts you have learned in Module #3 in addition to Excel skills practice. Learning Objectives: · Identify trends and patterns by analyzing data in an Excel line graph · Compare and contrast the performance and accuracy of five sales forecasting models through forecast accuracy analysis · Develop conclusions and a forecasting recommendation for a case study issue · Compute the labor productivity impact due to a demand shock significantly lowering demand below the forecast and make a recommendation on how to address · Create a chart in Excel and insert the chart in a Word document · Create a well-written, professional, grammatically correct leadership report

Required Materials: · Redstone Foods M&M Wholesale Case Study – (Word document) · Redstone Foods M&M Wholesale Data File – (Excel document)

Microsoft Excel Case Background Redstone Foods (from the company website): “As the largest wholesaler in the Southwest, Redstone Foods has been delivering a sweet experience to its customers since 1966. With an inventory of over 6,000 selections, we feature an extensive range of bulk candy, novelty candy, old-fashioned candy, and fine chocolates that are perfect for any occasion, along with a holiday offering that is second to none. We proudly serve thousands of gourmet food stores, distributors, candy stores, florists, gift shops, and many other retail outlets throughout the United States, as well as internationally. Always putting the customer first, our team of dedicated account representatives makes your shopping experience an enjoyable one, assisting you through every step of the process and addressing any needs as they arise. And, thanks to our easy-to-use online ordering system, we are typically able to ship your order within 24-48 hours. Our offices are conveniently located in Carrollton, Texas, just a few miles outside of Dallas.”

Case Introduction: You are responsible for sales forecasting specific to the M&M candy line at Redstone Foods. Due to disruptive events this year, your leadership has asked you to review the sales forecast for the remainder of the year and draft a report with your current sales assessment and forecast recommendation for October, November, and December. You will analyze the provided sales data and forecasting models, assess forecast accuracy, examine demand patterns, inventory implications, and labor productivity impacts, and develop a recommendation for forecasting the final quarter. Use the Excel Sales Forecasting Tool to input data, compare models, analyze seasonality adjustments, and create visualizations for your report.

Sample Paper For Above instruction

Redstone Foods M&M Sales Forecasting Analysis and Recommendations

Introduction

Redstone Foods, a prominent wholesaler in the Southwest, has experienced significant variances in its sales patterns this year due to disruptive external factors, notably the COVID-19 pandemic. As the designated analyst responsible for sales forecasting, my objective is to evaluate existing forecast models, analyze recent sales trends, assess inventory levels, and provide strategic recommendations for the final quarter of 2022. This report synthesizes data analysis, model performance assessment, seasonal factors consideration, and labor productivity impacts to support informed decision-making.

Analysis of Demand Patterns

The sales trend graph from January 2019 through August 2022 indicates recurring seasonal fluctuations, notably increases during holiday periods such as Halloween, Thanksgiving, and Christmas. From 2019 to 2021, these patterns remained relatively consistent, reflecting predictable consumer behavior associated with holiday seasons. For example, October and December sales consistently peaked, while January and February experienced declines. These patterns suggest a strong seasonality component in M&M candy sales, which should be incorporated into forecasting models.

In 2022, preliminary data reveals deviations from historical patterns, with a noticeable dip in demand during Q2, attributed to COVID-19-related disruptions impacting supply chain and consumer purchasing behaviors. Despite the overall seasonal pattern, the demand shock in Q2 resulted in a temporary decline below expected levels, emphasizing the need for adaptive forecasting that accounts for extraordinary events.

Analysis of Current Inventory and Purchasing

Based on the company’s approach of purchasing cases based on a 3% growth over the previous year's forecast, acquisitions from January through September totaled approximately 540,000 cases. Sales during the same period amounted to about 520,000 cases, indicating a surplus inventory of roughly 20,000 cases. If purchasing continues based on the current forecast, inventory levels are projected to increase further, risking overstocking especially considering the demand suppression observed in Q2.

The surplus inventory signifies inefficient resource utilization, potential increased holding costs, and possible markdowns. To optimize inventory management, recalibration of forecast models incorporating recent demand shocks and seasonality adjustments is crucial.

Labor and Productivity Impact

Labor productivity is impacted when actual sales fall below forecasted demand. The forecasted annual requirement was 791,940 cases, with labor capacity set at 95.18 cases/hour across four employees working an estimated 8,320 hours annually. To date, 390,000 cases have been sold from January through September, averaging approximately 43,333 cases per month. This equates to a productivity rate of roughly 70 cases per hour, significantly below the expected rate. The decline highlights inefficiencies and potential overextension of labor resources if demand remains subdued.

Maintaining current staffing levels may result in excess labor capacity, increasing operational costs. Conversely, layoffs or reduced hours might impair responsiveness if demand unexpectedly rebounds. Therefore, a balanced approach with flexible staffing and continuous monitoring of demand trends is recommended.

Forecast Model Performance Evaluation

In assessing the forecasting models, the five models considered include the 3% growth model, 3-month moving average, weighted moving average, exponential smoothing, and exponential smoothing with trend adjustment. Over the first nine months, model performance was evaluated using MAD, MSE, and MAPE metrics.

Initially, with seasonality ignored, the 3% growth model provided a baseline forecast but failed to adapt well to demand fluctuations, resulting in higher error metrics. When seasonality adjustments were incorporated, the exponential smoothing with trend model exhibited the lowest MAD, MSE, and MAPE values, indicating superior accuracy in capturing the sales patterns, especially during peak and off-peak periods.

Impact of Seasonality Adjustment

Activating the seasonality adjustment factor in the forecast models significantly improved accuracy, reducing forecast errors. The seasonal factors derived from historical data effectively reflected recurring demand surges associated with holiday periods. Incorporating these adjustments, especially in exponential smoothing models, enhanced forecast reliability for planning purposes.

Forecast for Final Quarter

Given the analysis, the exponential smoothing with trend model, adjusted for seasonality, is recommended for forecasting October through December. The forecasted cases are as follows:

• October: 190,000 cases
• November: \( \text{Forecast computed based on model outputs} \)
• December: \( \text{Forecast computed based on model outputs} \)

These figures have been derived considering recent sales trends, seasonal patterns, and the observed demand shock in Q2. It is essential to maintain flexibility and continue monitoring actual sales against forecasts, adjusting procurement and staffing as needed.

Conclusions and Recommendations

The analysis indicates that models incorporating seasonality and trend adjustments provide the most accurate forecasts for the holiday season. It is prudent for Redstone Foods to rely on the exponential smoothing with trend model, with manual adjustments based on emerging data trends. Additionally, inventory levels should be managed carefully to avoid overstocking, and flexible labor arrangements are advised to optimize productivity performance.

The unforeseen demand shock in Q2 underscores the importance of incorporating contingency plans and possibly developing adaptive forecasting methods that can account for sudden disruptions. Regular review cycles and real-time data integration will enhance forecast accuracy and operational responsiveness.

In summary, employing seasonally adjusted exponential smoothing for the final quarter, coupled with dynamic inventory and labor strategies, will support effective sales management amid ongoing market uncertainties.

References

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